Modeling Post-cholecystitis Complication Risk from Perioperative Liver Function and Immune-inflammation Indicators

Abstract

Acute calculous cholecystitis (ACC) often triggers transient perioperative elevations in liver enzymes and systemic inflammation, yet existing complication-prediction tools seldom incorporate dynamic biomarker changes. Our goal was to establish and develop, using internal validation, a multivariable risk model that incorporates perioperative variations in liver function tests (LFTs) and the Systemic Immune-Inflammation Index (SII) in order to predict Clavien–Dindo grade ≥II complications following cholecystectomy for ACC. In this retrospective cohort study at a tertiary academic center (January 2022–December 2024), we analyzed 260 adult patients undergoing laparoscopic or open cholecystectomy for ACC. We calculated Δ-values (day 1 minus baseline) for alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, and SII (platelet×neutrophil/lymphocyte). Multivariable logistic regression with backward stepwise selection was used to derive the final model, which included ΔALT, ΔAST, Δbilirubin, ΔSII, age, American Society of Anesthesiologists (ASA) status, and operative duration. Internal validation employed 1 000 bootstrap replications. The model demonstrated good discrimination (optimism-corrected area under the curve, 0.82; 95% CI, 0.77–0.87) and excellent calibration (slope, 0.95; intercept, –0.02). Significant predictors included ΔALT, ΔAST, Δbilirubin, and ΔSII, along with age, ASA III status, and longer operative duration. The decision-curve analysis demonstrated net benefit across threshold probabilities of 5% to 40%, with 15 additional true positives per 1 000 at the 20% threshold. Integrating dynamic perioperative changes in LFTs and SII with key clinical factors yields a robust risk prediction model for postoperative complications after ACC surgery.